It must feel pretty good to be Nvidia co-founder and CEO Jensen Huang right now. Ever since deep learning caught fire a few years ago, really kicking off the current machine learning and artificial intelligence revolution, Nvidia has been a critical part of the conversation. After all, GPUs were a big part of the reason deep learning actually worked this time around, and they’re still the engine that powers the (vast?) majority of AI models.
(On a related note, there are a lot of high-profile conferences happening this week: Nvidia GTC, Microsoft Build, Dell EMC World, OSCON and OpenStack Summit. Did I miss any?)
The company has been on a two- or three-year mission to make machine learning as popular as possible (not that it needed much help), and to position its GPUs as the default hardware platform for doing it. It’s doing pretty well, too: literally (I’m pretty certain) every popular deep learning library and framework is built to run on Nvidia GPUs with its CUDA programming model.
You can’t blame Nvidia for doubling down on machine learning and trying stake its claim as the only processor game in town. Because the good times are not guaranteed to last, especially if Nvidia gets lazy on the innovation front or lays off on the marketing. Lurking in the shadows (if that’s possible), is Intel
, which would love to see machine learning workloads run on its line of CPUs, FPGAs and other next-generation gear.
That’s not to mention Google’s decision to build its own AI chips, called Tensor Processing Units
, which it claims are largely superior to GPUs for its purposes. This is important: cloud providers like Google, AWS and Microsoft are major purchasers of data center hardware, and are all investing heavily in AI. If those companies, or even two of them, aren’t buying GPUs in bulk, then Nvidia is leaving a lot of money on the table.
Would I love to be Nvidia right now? Absolutely. But I’d also spend a lot of time thinking about my next moves to get out in front of the competition, or at least to make sure it remains the stuff of research labs and niche deployments.